Marketers know that a customer’s journey from their first exposure to the brand to a conversion event will generally involve a number of touchpoints along the way. The trick is to be able to understand the role that each of these channels and touchpoints played in the desired outcome of the customer making a final purchase.
Yet accurately attributing credit for sales and conversions to the different channels and touchpoints used in a campaign can be problematic, despite the advanced and rapidly improving tools available to marketers.
It’s easy to fixate on the wrong data, to misinterpret what the data means, or to use the wrong tools for the job. This article addresses some of the most common attribution pitfalls we’ve observed in the market.
Taking Google Analytics web traffic data at face value
Many marketers use Google Analytics web traffic data as the primary tool for measuring campaign success. But too few of them dig deep enough into the data to get a complete picture of the journey; instead, they simply attribute success to the final touchpoint.
For example, a person first visits a site from a Facebook advert and then comes back later and converts via a Google Search, analytics will only give credit to Google and discard the original source being Facebook.
In reality, to fully understand which digital channels are delivering the most value, you have to look at the full customer journey leading to a conversion, which usually involves multiple touch points.
Thus, relying only on Google Analytics will give you a skewed view of performance since it will generally favour lower funnel channels such as search over brand awareness campaigns.
Overcounting conversions from tags
It is easy to put tags in place on programmatic platforms, including Google and social media channels.
You can use tags to measure post-view and post-click data, solving the problem of missing times the customer was exposed to the brand without clicking through to the website.
For instance, you could credit an ad with the conversion if a person converted within 30 days of clicking through.
When it comes to views, you would use a shorter timeframe, perhaps 24 hours. If the person sees the ad, and converts within 24 hours without clicking through, the ad can still be credited with the sale. But there is a danger of overcounting conversions because different platforms and media sources in a conversion path might claim credit.
The Facebook tag might count 100 conversions, of which only 20 were last in the conversion path and 80 were earlier. Four other media sources with tags may also claim credit for the conversions.
If each is given full credit for the conversion, you are effectively counting five conversions for a single sale.
Errors in assisted conversions in analytics
Marketers can use the filtering tools in Google Analytics to look at the channels along every conversion path and the number of conversions attributed to every path.
A conversion path might follow the route described above: Facebook ad, display, organic search, paid search. You can easily see that there were 10 media channels within one conversion path.
Yet, you will still encounter two significant challenges: firstly, the customer might not visit the website during early exposures to the brand such as when viewing an online video or engaging on social media, and secondly, it is difficult to allocate credit to the role each channel played in conversion.
As with mistake 2, there is a danger of overcounting conversions if multiple traffic sources in the conversion are all granted equal credit.
For brands with larger budgets, premium media can be tracked in the same way as Google or social via ad-serving tools such as DoubleClick Campaign Manager or Sizmek. This gives you more power to track the role of every source that delivered a visit to the website in converting a customer.
The easiest solution for a smaller business is to refer to assisted conversions within Google Analytics and assign a value to first, middle and end conversions. Be careful, however, not to give every channel equal credit as it will lead to an over count of total conversions.
Putting too much faith in post-view conversions
Post-view data from an agency or a programmatic platform can sometimes overinflate the metrics for a campaign.
For example, if you have a scenario where a 1,000 people visit your site in one day to do trip research and of those 10 people return within 24 hours to make a booking, it would be considered normal consumer behaviour since people tend to do research on multiple sites before converting.
The challenge is that when you are using re-marketing, a large portion of those 1,000 people are likely to see an ad while they continue their research.
With post view conversion data, the re-marketing platform will claim credit for all 10 bookings as they can claim its logical to get credit given they made a booking within 24 hours of seeing the ad. They are, however, ignoring that those people were already looking to book based on an earlier research visit to the website.
This is a much stronger indicator of intent to book than seeing an ad that they did not click. We find that this is often not considered when evaluating the performance of a re-marketing campaign and creates a false view of success.
Not having the right tools and models to correctly credit traffic sources
Many marketers lack a model for valuing the contribution of each traffic source in their conversion paths. They don’t use the available technology to make sense of the data, limiting their ability to optimise campaign decisions.
Yet the cost of these tools is relatively low compared to their potential to help a brand reduce wasted spend and improve return on investment by spending on the right channels.
Larger companies will find it worthwhile to invest in higher-end solutions like Bionic, Zapier, Adinton and the Google premium offerings (Analytics360, DoubleClick Campaign Manager and DoubleClick Search). They enable brands with multimillion-rand campaigns to make more informed optimisation decisions.
Some of the functionality from the top-end tools might include the ability to integrate digital campaign data with CRM platforms to offer a more complete view of the journey, including offline conversion events from digital marketing, or functionality to model and weight the role each touchpoint played in conversion.
Adinton, for example, measures visitor engagement during each website visit and uses an algorithm to determine the relative contribution of each traffic source to the outcome.
If there are five sources claiming credit, the solution can tell you that based on the level of engagement the tool tracked for each visit the first should get 20%, the second 10%, and so on. You can use this data to determine in which media platforms you are over- and underinvesting.
Attribution is key for ROI
Attribution is a complex discipline and requires some investment in terms of time and money. But the alternative is to spend marketing and advertising budget inefficiently.
Using the right tools to understand how different channels lead to conversion enables you to get the best possible ROI from your spend at a time most brands need to optimise costs as far as possible.